Stabilizing Video to Reduce Camera and Face Movement

ABSTRACT

The subject matter described in this disclosure can be embodied in methods and systems for stabilizing video. A computing system determines a stabilized location of a facial feature in a frame of video accounting for its location in a previous frame. The computing system determines a physical camera pose in virtual space and maps the frame into virtual space. The computing system determines an optimized virtual camera pose using an optimization process that determines (1) a difference between the stabilized location of the facial feature and a location of the facial feature when viewed from a potential virtual camera pose, (2) a difference between the potential virtual camera pose and a previous virtual camera pose, and (3) a difference between the potential virtual camera pose and the physical camera pose. The computing system generates the stabilized view of the frame using the optimized virtual camera pose.

TECHNICAL FIELD

This document discusses stabilizing video to reduce camera and facemovement.

BACKGROUND

Various types of recording devices, such as video cameras andsmartphones, include image sensors that can be used to record video. Avideo may be generated by capturing a sequence of images, which areoften called video “frames” and which are typically captured at adefined frame rate (e.g., thirty frames per second). The capturedsequence of frames may be presented by a display device at the sameframe rate, and the switching from a presentation of one frame to apresentation of another frame may go largely unnoticed by humans, suchthat the display may appear to present actual motion rather than asequence of quickly-switching images.

Recording devices are sometimes subject to unintentional movement duringrecording, for example, shaking that results from the recording devicebeing handheld by a person or attached to a moving vehicle. Suchmovement can have a particularly significant effect on a video when themovement is rotational and the scene being captured is far away from therecording device.

Various techniques can stabilize video to limit unintentional movementof a recording device. One technique to stabilize video is mechanicalstabilization, in which mechanical actuators counteract external forces.Mechanical stabilization can be implemented by mounting the recordingdevice to a secondary device that stabilizes movement of the entirerecording device (e.g., a gimbal). Mechanical stabilization can also beimplemented by integrating actuators within the recording device tostabilize the camera or portions thereof with respect movement of themain body of the recording device. Another technique to stabilize videois digital video stabilization, in which a computer analyzes video thathas been recorded and crops the captured frames in a manner thatproduces a partially-zoomed-in version of the video that is stabilized.Mechanical stabilization and digital video stabilization techniques maybe combined.

SUMMARY

This document describes techniques, methods, systems, and othermechanisms for stabilizing video to reduce camera and face movement. Ingeneral, the mechanisms described herein can generate a stabilizedversion of a video by determining a virtual recording pose (a virtualcamera location and orientation) that provides a more stable recordingexperience than the actual pose of the physical camera. The virtualrecording pose may be determined to counteract not only undesiredphysical movement of the camera, but also movement of a face in thescene. A computerized process can warp a frame that was captured by thephysical camera so that the frame appears to have been captured from thevirtual recording pose rather than the actual pose of the physicalcamera, with the virtual recording pose being laterally offset invirtual space from the pose of the physical camera. This process may berepeated for each frame to produce a stabilized video.

Stabilizing movement of a face in addition to movement of a camera canprovide better stabilization results in various circumstances. Forexample, suppose that a user is taking a video with a front-facingcamera of a smartphone (e.g., a “selfie) while riding in a vehicle. Thevehicle may cause both the camera and the user to bounce. A videostabilization mechanism that stabilizes only physical movement of thecamera may actually be counterproductive in such a situation because theuser's face may continue to bounce even if the camera location werestabilized.

The technology described herein can stabilize video to minimizeunintentional movement of both the camera and one or more objects. Assuch, the need for mechanical stabilization may be reduced oreliminated, which can lower manufacturing expenses and the spacerequired to house a camera in a recording device. Alternatively,stabilization may be enhanced if both mechanical and digital videostabilization are used. Another benefit is that a user of a recordingdevice may not have to concentrate on stabilizing movement of therecording device or a subject of the video, and may focus on otheraspects of the video-taking experience.

As additional description to the embodiments described below, thepresent disclosure describes the following embodiments.

Embodiment 1 is a computer-implemented video stabilization method. Themethod comprises receiving, by a computing system, a video stream thatincludes multiple frames and that was captured by a physical camera. Themethod comprises determining, by the computing system and in a frame ofthe video stream that was captured by the physical camera, a location ofa facial feature of a face that is depicted in the frame. The methodcomprises determining, by the computing system, a stabilized location ofthe facial feature, taking into account a previous location of thefacial feature in a previous frame of the video stream that was capturedby the physical camera. The method comprises determining, by thecomputing system and using information received from a movement ororientation sensor coupled to the physical camera, a pose of thephysical camera in a virtual space. The method comprises mapping, by thecomputing system, the frame of the video stream that was captured by thephysical camera into the virtual space. The method comprisesdetermining, by the computing system, an optimized pose of a virtualcamera viewpoint in the virtual space from which to generate astabilized view of the frame, using an optimization process. Theoptimization process determines a difference between the stabilizedlocation of the facial feature and a location of the facial feature in astabilized view of the frame viewed from a potential pose of the virtualcamera viewpoint. The optimization process determines a differencebetween the potential pose of the virtual camera viewpoint in thevirtual space and a previous pose of the virtual camera viewpoint in thevirtual space. The optimization process determines a difference betweenthe potential pose of the virtual camera viewpoint in the virtual spaceand the pose of the physical camera in the virtual space. The methodcomprises generating, by the computing system, the stabilized view ofthe frame using the optimized pose of the virtual camera viewpoint inthe virtual camera space.

Embodiment 2 is the computer-implemented video stabilization method ofembodiment 1, further comprising presenting, by the computing system,the stabilized view of the frame on a display of the computing system.

Embodiment 3 is the computer-implemented video stabilization method ofembodiment 1, wherein the movement or orientation sensor comprises agyroscope.

Embodiment 4 is the computer-implemented video stabilization method ofembodiment 1, wherein the computing system determines the location thefacial feature of the face that is depicted in the frame based onlocations of multiple respective facial landmarks that are depicted inthe frame. Moreover, the computing system determines the differencebetween the stabilized location of the facial feature and the locationof the facial feature in the stabilized view of the frame by measuringdeviations between locations of the multiple facial landmarks in thestabilized view of the frame and the stabilized location of the facialfeature.

Embodiment 5 is the computer-implemented video stabilization method ofembodiment 1, wherein the optimization process comprises a non-linearcomputational solver that optimizes values for multiple respectivevariables.

Embodiment 6 is the computer-implemented video stabilization method ofembodiment 1, wherein the optimization process determines an amount ofundefined pixels in the stabilized view of the frame that is generatedusing the potential pose of the virtual camera view point in the virtualspace.

Embodiment 7 is the computer-implemented video stabilization method ofembodiment 1, wherein the optimization process determines a differencebetween (a) an offset of a principal point of the stabilized view of theframe that is generated using the potential pose of the virtual cameraview point in the virtual space, and (b) an offset of a previousprincipal point of a previous stabilized view of the frame that wasgenerated using the previous pose of the virtual camera viewpoint in thevirtual space.

Embodiment 8 is the computer-implemented video stabilization method ofembodiment 1, wherein generating the stabilized view of the frameincludes mapping a subset of scanlines of the frame to perspectivesviewed from the optimized pose of the virtual camera viewpoint, andinterpolating other of the scanlines of the frame.

Embodiment 9 is the computer-implemented video stabilization method ofembodiment 1. The method comprises selecting the face that is depictedin the frame of the video stream that was captured by the physicalcamera as a face to track from among multiple faces depicted in theframe of the video stream that was captured by the physical camera by:(i) selecting the face based on sizes of each of the multiple faces,(ii) selecting the face based on distances of each of the multiple facesto a center of the frame, or (iii) selecting the face based on distancesbetween a face selected for tracking in a previous frame and each of themultiple faces.

Embodiment 10 is the computer-implemented video stabilization method ofembodiment 1, wherein the optimized pose of the virtual camera viewpointhas a different location and rotation in the virtual space than the poseof the physical camera.

Embodiment 11 is directed to one or more computer-readable deviceshaving instructions stored thereon, that when executed by one or moreprocessors, cause the performance of actions according to the method ofany one of embodiments 1 through 10.

Embodiment 12 is directed to a system that includes one or moreprocessors and one or more computer-readable devices having instructionsstored thereon, that when executed by the one or more processors, causethe performance of actions according to the method of any one ofembodiments 1 through 10.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 shows frames of a video stream and movement of objectsrepresented within the frames of the video stream.

FIG. 2 shows viewpoints of physical and virtual cameras in virtualspace, and a frame mapped into virtual space.

FIGS. 3A-E show a flowchart of a process for stabilizing video to reducecamera and face movement.

FIG. 4 is a conceptual diagram of a system that may be used to implementthe systems and methods described in this document.

FIG. 5 is a block diagram of computing devices that may be used toimplement the systems and methods described in this document, as eithera client or as a server or plurality of servers.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

This document generally describes stabilizing a video to reduce cameraand object movement. Techniques described herein warp frames of a videoso that the frames appear to have been taken from a pose of a stabilizedvirtual camera viewpoint rather than a pose of the physical camera thatcaptures the frames. The process that determines the pose of the virtualcamera viewpoint can account for a current pose of the physical camera,a previous pose of the virtual camera viewpoint, and a location of aface in the scene.

The location of the human face in the scene may matter, because thehuman face may move with respect to a camera, even if the location ofthat camera were stabilized (e.g., either mechanically or using digitalstabilization techniques). Movement of a person's face may beparticularly noticeable when the person is close to the camera, such aswhen the person is taking a “selfie,” because the person's face in suchcircumstances may occupy a sizable portion of the frame.

The techniques described herein, can calculate the location of aperson's face in a video, determine how that face moves from one frameto the next, and determine a “stabilized” location of the person's facein a most-recently-received frame of the video. The stabilized locationof the person's face may be a location at which the person's face wouldbe located if movement of the person's face were smoothed, such as ifsomeone had grabbed that person by the shoulders to limit any shakingand slow any sudden movements. The video stabilization process, inselecting a pose for the virtual camera viewpoint, may take into accountthe stabilized location of the person's face.

More broadly, the video stabilization process can take into accountmultiple different factors in selecting the pose for the virtual cameraviewpoint. A first factor is a pose of the physical camera, asdetermined using one or more sensors of the camera that identifymovement and/or rotation of the camera (e.g., a gyroscope). A secondfactor is a pose determined for the virtual camera in the last frame.Absent other factors, the determined pose of the virtual cameraviewpoint would likely be located somewhere between the pose of thephysical camera and the previous pose of the virtual camera viewpoint.

One of these other factors is a distance between the stabilized locationof the person's face (determined as discussed above) and the location ofthe person's face in the video frame, when the video frame is warped sothat it appears to have been taken from the perspective of the virtualcamera viewpoint. As a simple illustration, a person may have moved hisor her face to one side very quickly, which may cause the recordingdevice to determine that a stabilized location of the face should be inbetween the previous location of the face and the actual location of theface. The impact of the sudden face shift on the recorded video may bereduced by moving the virtual camera viewpoint in the same directionthat the face moved, so that the movement of the person's face, at leastwithin the stabilized video, is reduced.

As such, the video stabilization techniques described herein can accountfor multiple different factors to select an optimized pose for a virtualcamera viewpoint, and once that pose has been selected, can warp a frameof video so that the video can appear to have been taken from theoptimized pose of the virtual camera viewpoint rather than the pose ofthe physical camera (with both the location and orientation of thevirtual camera viewpoint being different from the location andorientation of the physical camera in virtual space, in some examples).The process may be repeated for each frame of a video, to generate avideo that appears to have been captured from a virtual camera thatmoved more in a more stable manner than the physical camera. The processmay not require analysis of future frames, and therefore may beperformed in real-time as the video is being captured. As such, thevideo that appears on a display of the recording device while therecording device is recording video may be the stabilized video.

The following description explains such video stabilization techniquesin additional detail with respect to the figures. The description willgenerally follow the flowchart of FIGS. 3A-E, which describes a processfor stabilizing video to reduce camera and face movement. Thedescription of that flowchart will reference FIGS. 1 and 2, toillustrate various aspects of the video stabilization techniques. FIG. 1shows frames of the video and the movement of objects from one frame tothe next. FIG. 2 shows the hardware that records the video, andillustrates how that hardware and the virtual camera viewpoint changeover time as frames are captured.

Referring now to the flowchart that begins in FIG. 3A, at box 310 acomputing system receives a video stream that includes multiple frames.For instance, the computing system 220 that is depicted in FIG. 2 as asmartphone may record video using a front-facing camera 222. Therecorded video may comprise a video stream 110, which is illustrated inFIG. 1 as including multiple component frames 120, 130, and 140. FIG. 1shows an example in which the video stream 110 is being recorded inreal-time, and frame 140 represents a most recently-captured frame, withframe 130 being a last frame that was captured, and frame 120 being theframe that was captured before that. Video stream 110 may includehundreds or thousands of frames that were captured at a pre-determinedframe rate, such as 30 frames-per-second. Computing system 220 may storeeach of the frames to memory as they are captured. In some examples,video stream 110 may have been previously recorded, and frame 140 mayrepresent a frame in the middle of the video stream 110.

At box 312, the computing system selects a target time for a selectedframe. The selected frame may be the most-recently-captured frame inthose examples in which the video stabilization is being performed inreal time (e.g., frame 140 in FIG. 1), or may represent a frame in themiddle of the video in those examples in which the video stabilizationis being performed after the video has been captured. In some examples,the target time may defined as the beginning of the exposure durationfor the frame, the end of the exposure duration for the frame, or someother time during the exposure duration for the frame. For instance, thecomputing system may define the target time for the selected frame asthe middle of the exposure duration for the frame (box 314).

At box 316, the computing system selects a face depicted in the frame asa face to track from among multiple faces depicted in the frame. Forexample, the computing system may analyze the frame to identify eachface in the frame and may then determine which of the identified facesto track. Regarding frame 140 in FIG. 1, the computing system may selectto track face 150 rather than face 160 due to various factors, such asthose described with respect to boxes 318, 320, and 322.

At box 318, the computing system selects a face based on sizes of eachof the multiple faces. For example, the computing system may determine asize of a bounding box for each face, and may select a face with thelargest bounding box.

At box 320, the computing system selects a face based on distances ofeach of the multiple faces to a center of the frame. For example, thecomputing system may identify a location of each face (described in moredetail below with respect to box 330), and may determine a distancebetween that respective face and a center of the frame. The face that isclosest to the center of the frame may be selected.

At box 322, the computing system may select a face based on distancesbetween a location of the face selected in a previous frame and alocation of each face in the current frame. This operation may helpensure that the system tracks the same face from frame to frame as theface moves around in the video.

The computing system may weight the operations of boxes 318, 320, and322 the same or differently to produce an interest value for each face,and may compare the interest values of the faces to identify the facewith the highest interest value. A threshold interest value can bedefined, so that if no face has an interest value that exceeds thethreshold, no face will be selected. The face-selection process canaccount for other factors, such as orientations of the faces, whethereyes are open in the faces, and which faces show smiles. Theface-selection process can consider any combination of one or more ofthe above-described factors.

At box 330, the computing system may determine a location in the frameof a facial feature to track from the selected face. An example facialfeature to track is the center of the face, although the computingsystem may track another facial feature, such as a person's nose ormouth center. Determining the center of a person's face can involve theoperations of boxes 332, 334, and 336.

At box 332, the computing system identifies a bounding box for theselected face. The computing system may perform this identificationusing a face information extraction module.

At box 334, the computing system uses the center of the face as thefacial feature. The computing system may determine the center of theface by identifying the location of multiple landmarks on the face anddetermining a mean location of those landmarks. Example landmarksinclude eye locations, ear locations, nose locations, eyebrow locations,mouth corner locations, and a chin location.

Although the operations of box 334 are described with reference to frame140, this center-of-face determination process may be performed withrespect to each frame in the video as that frame is stabilized. Becausethe depiction of frame 140 in FIG. 1 includes multiple annotations,annotations that illustrate the center-of-face determination process ispresented instead for frame 130 in FIG. 1, although it should beunderstood that the center-of-face determination process may be similarwhen applied to frame 140. Determining the center of face 150 in frame130 can include the computing system identifying multiple landmarks onface 150, with the identified landmarks illustrated withindotted-rectangular boxes 170. The annotations that accompany frame 130illustrate that the computing system has identified the location of eyesand mouth corners, although the computing system may identify other facelandmarks. Through analysis of these landmark locations 170, thecomputing system determines that location 180 represents the meanlocation of the landmarks 170.

At box 336, the computing system determines the orientation of the face,and uses the face orientation to determine whether the face is in aprofile mode. In some examples, the computing system may not track thelocation of the face if it is determined to be in profile mode.

At box 340, the computing system determines a stabilized location of thefacial feature. Continuing the above-discussed example in which the facecenter the facial feature that is tracked, the computing systemdetermines a stabilized center of the face (represented as location 182in frame 140 of FIG. 1). The stabilized center of the face can representa location at which the face center would be located had the face movedsmoothly from the previous frame to the current frame. As such, thestabilized location of the face center may be selected to be not too faraway from the real center of the face (represented as location 184 inframe 140 of FIG. 1), but the stabilized location of the face center iskept still if possible. As such, identifying the stabilized location ofthe face center can involve an optimization process that accounts formultiple different factors, as described with respect to boxes 342, 344,and 346.

At box 342, determining the stabilized location 182 of the facialfeature accounts for the distance between a potential stabilizedlocation and the actual location 184 of the facial feature (e.g., themean of face landmarks). For example, the computing system may considerhow far a potential stabilized face center strays from the actual,determined center of the face, to ensure that the stabilization does notattempt to stabilize the face so strongly that the video would departsubstantially from the actual depiction of the face in the video. Theterm that accounts for this factor is referred to as E_follow, whichmeasures how far away the stabilized two-dimensional center of thecurrent frame H(T) is from the real landmark center C(T) that isdetermined as the mean of all 2D landmarks.

At box 344, determining the stabilized location 182 of the facialfeature accounts for the distance between a potential stabilizedlocation of the facial feature and a previously determined location 180of the facial feature. For example, the computing system may considerhow far a potential stabilized center strays from the location 180 ofthe last determined center of the face, to ensure that the stabilizationdoes not attempt to track the current location of the face so stronglythat the face makes sudden movements in the video. The term thataccounts for this factor is referred to as E_smoothness, which measuresthe change between H(T) and H(T_pre), where H(T_pre) is the estimated 2Dhead center of the previous frame.

At box 346, determining the stabilized location of the facial featureaccounts for a constraint on a distance between a potential stabilizedlocation of the facial feature and the determined location of the facialfeature. This factor is imposed as a hard constraint|H(T)−C(T)|<CroppedRange, so that C(T) moves within a valid range aroundH(T) which does not cause undefined regions.

An example process that combines these various terms to determine astabilized location of the facial feature can be summarized asE_H(T)=w_1*E_smoothness+w_2*E_follow, such that|H(T)−C(T)|<CroppedRange. The values w_1 and w_2 are used to weight thesmoothness and follow factors.

At box 348, the computing system determines a pose of the physicalcamera in virtual space. For the first frame of a video, the pose of thephysical camera is referred to as R(t) and may be initialized with zerorotation and offset. The computing system may thereafter analyze signalsreceived from one or more movement or rotation sensors to determine howthe pose of the physical camera changes and a location for asubsequently-captured frame. For example, the computing system mayreceive a signal from a gyroscope at a high frequency (e.g., 200 Hz),and may use information in the signal to determine how the pose of thecamera is changing and a current pose of the camera (where the currentpose of the physical camera may have a different orientation and/orlocation than the initialized pose of the physical camera). In someexamples, the computing system alternatively or additionally uses anaccelerometer to determine the pose of the physical camera. The cameraand the one or more movement or rotation sensors may be physical coupledto each other, such that the camera and the one or more sensors have thesame movement and pose. For instance, the camera and the one or moresensors may be coupled to the same housing of a smartphone. The pose ofthe physical camera may be represented as a Quaternion representation (a4D vector) that defines the camera pose in virtual space. The pose ofthe physical camera in virtual space is represented in FIG. 2 by camera214. Determining poses of objects in virtual space can include assigningcoordinates and orientations to the objects in a common coordinatesystem, and does not require generating a visual representation of theobjects.

At box 350, the computing system maps the frame to virtual space. Forexample, the computing system may apply coordinates to the frame torepresent locations of portions of the frame, such as the corners of theframe, with respect to the location in the virtual space of the physicalcamera. This mapping may be performed by constructing a projectionmatrix that maps the real world scene to the image, as illustrated inFIG. 2 by frame 230. Mapping the frame to the virtual space may accountfor various factors, described with respect to boxes 352 and 354.

At box 352, mapping the frame to virtual space accounts for the pose ofthe physical camera in virtual space. For example, the physical camerapose can be used to determine that the location of the frame should bein front of the physical camera with a principle point (e.g., a center)aligned with the orientation of the physical camera.

At box 354, mapping the frame to virtual space accounts for the focallens of the physical camera and a current zoom setting of the physicalcamera. As an example, a frame that was captured using a camera withouta fisheye lens and that was not zoomed in should span a larger portionof the virtual space in front of the physical camera than a frame thatwas captured using a camera without a fisheye lens that was zoomed in.

This process of mapping the frame into the virtual space can berepresented by the equation P_(i, j_T)=R_(i,j_T)*K(i,j_T), where i isthe frame index and j is the scanline index. R_(i, j_T) represents thepose of the physical camera, also referred to as the camera extrinsicmatrix (a rotation matrix) obtained using the gyroscope information.K(i,j_T)=[f 0 Pt_x; f 0 Pt_y; 0 0 1] is the camera intrinsic matrix,where f is the focal length of current frame and Pt is the 2D principalpoint which is set to the image center.

At box 360, the computing system determines an optimized pose of avirtual camera viewpoint in the virtual space from which to generate astabilized view of the frame using an optimization process. Thisoptimization may be performed using a non-linear motion filteringengine, and can select a virtual camera viewpoint that smooths rotationsand translations of the virtual camera viewpoint with respect to thephysical camera. Selecting the optimized pose of the virtual cameraviewpoint can involve selecting a position in virtual space of thevirtual camera viewpoint and an orientation of the virtual cameraviewpoint, one or both potentially being different from the location andorientation in virtual space of the physical camera.

The optimized pose of the virtual camera is illustrated in camera 212 inFIG. 2, and that pose can be determined using the optimization processthat accounts for multiple factors, such as a pose of the physicalcamera (illustrated as camera 214 in FIG. 2), a pose of the virtualcamera for a previous frame (illustrated as camera 210 in FIG. 2), and adistance between an actual location of a facial feature (e.g., facecenter 280 in FIG. 2) and a stabilized location of the facial feature(e.g., stabilized face center 282 in FIG. 2).

An example equation to determine the optimized pose of the virtualcamera isE_V_0(T)=w_1*E_center+w_2*E_rotation_smoothness+w_3*E_rotaton_following+w_4*E_distortion+w_5*E_undefined_pixel+w_6*E_offset_smoothness.The terms included within this equation (e.g., E_center andE_rotation_smoothness) can an example virtual camera pose as an inputand can output a value indicating suitableness of the virtual camerapose to the particular term. The virtual camera pose (also calledvirtual camera viewpoint herein) may be represented as V_0(T)=[R_v(T),O_v(T)] where R_v(T) is the virtual camera extrinsic matrix (a rotationmatrix) and O_v(T) is a 2D offset of the virtual principal point Pt_v.

Selecting a given virtual camera pose can affect the value of each termin the above-illustrated equation, producing a resultant value forE_V_0(T). Thus, different virtual camera poses may be input into theabove-shown equation to determine different values for E_V_0(T). Insteadof inputting many different virtual camera poses into the above equationfor E_V_0(T) to identify the virtual camera pose with a best value, anonlinear solver, such as the Ceres solver, may be used to determine anoptimum virtual camera pose (e.g., a value that minimizes the value ofE_V_0(T)). Although E_V_0 is a function of T, it may also be representedas a function of R_v(T) and O_v(T) because the value of T affects thevalues of R_v(T) and O_v(T) which affect the value of E_V_0, andtherefore may alternatively be illustrated as E_V_0(R_v(T), O_v(T)).Additional detail regarding the determination of the optimized virtualcamera pose is provided with reference to boxes 360-380.

At box 360, the computing system determines whether the optimizationprocess is acting on the first frame of the video. If so, the computingsystem uses a virtual camera pose with zero rotation and zero offset asan initialization (box 364). If not, the computing system uses thevirtual camera pose from the previous frame in the optimization process(box 366).

At box 368, the optimization process determines a difference between (1)the stabilized location of the facial feature, and (2) a location of thefacial feature in a stabilized view the frame. This is the term of theoptimization process that accounts for movement of the face. The affectand operation of this factor can be illustrated with respect to FIGS. 1and 2.

As an illustration, FIG. 1 shows three frames 120, 130, and 140. Faces150 and 160 are represented in each of these frames, and are positionedin different locations with respect to each other in the various framesto illustrate aspects of the technology described herein. Frame 120shows initial locations of faces 150 and 160. Between frame 120 and 130,however, the camera may move (e.g., translate) to the right, causing thelocations of faces 150 and 160 in frame 130 to shift to the left withrespect to their locations in frame 120. In frame 140, the camera hasnot moved from the position it was at when it captured frame 130, butface 150 moved to the right in the frame 140 due to real-world movementof the face. (Face and camera movement would often occur simultaneously,but the movements are isolated to different frames in this illustrationfor ease of description.)

As described previously with respect to boxes 340-346, the computingsystem has determined that stabilized location 182 represents astabilized position of face 150, for example, a desired center of face150 that would stabilize movement of face 150 as it moves between frames130 and 140. Stabilized location 182 is also illustrated in FIG. 2,which shows frame 140 mapped into virtual space as frame 230. As shownin frame 140 and also frame 230, the actual center of the user's face inthe frames (determined based on face landmarks) is located to the rightof the stabilized location 182 of the user's face 150, due to the userhaving moved to the right during the transition from frame 140 to frame150.

FIG. 2 shows frame 230 from the perspective of physical camera 214, butthe view and location of certain objects in the frame can change if theframe 230 were viewed from the perspective of the virtual camera 212rather than the perspective of the physical camera 214. As anillustration, as the virtual camera 212 is moved around, the location ofthe stabilized location 182 may remain fixed but the location of face150 may move around in the frame, just as the location of an objectwithin your own field of vision may change if you step to the side andkeep looking towards the object. With such an ability to move virtualcamera 212 around to affect the location of face 150 in the image, anoptimal stabilization of face 150 may include locating the virtualcamera 212 so that the determined center of face 150, as viewed from theviewpoint of virtual camera 212, aligns with the stabilized location182. The optimization algorithm, however, can account for factors, andtherefore, the optimal location of the virtual camera 212 that isultimately selected may not be positioned so that the determined centerof face 150 perfectly aligns with the stabilized location 182.

At box 370, the location of the facial feature in the stabilized view ofthe frame may be represented using locations of multiple faciallandmarks in the stabilized view of the frame. For example, thecomputing system may determine the location of multiple facial landmarksand these locations collectively may represent the location of thecenter of the face 150. Computations that account for the location ofthe facial feature (e.g., the center of the face 150) need not actuallycompute the location of the facial feature, and can instead use datafrom which the location of the facial feature as indicated, such as thelocations of multiple landmark locations.

At box 372, the difference of box 368 is determined by taking a mean ofdistances between (1) the stabilized location of the facial feature and(2) the locations of the multiple facial landmarks in the stabilizedview of the frame. In other words, the computing system may not actuallycalculate the location of the facial feature in the stabilized view ofthe frame. Rather, the system may determine how far each of thelandmarks are in the stabilized view of the frame to the stabilizedlocation of the facial feature, and can identify a virtual camera posethat minimizes that distance among all landmarks.

Referring back to the equation for E_V_0(T), the operations of box 368represent E_center, which can measure the mean of deviations betweeneach projected landmark location on the virtual camera plane (e.g., theframe 140 viewed from the perspective of the virtual camera) and theestimated 2D head center H(T), which is the target head center on thevirtual camera plane. For each detected landmark I, the computing systemmay identify the scanline to which it belongs, the computing system maycompute the transformation to map the real image projected by P_(i,j) toP_v(T) for this scanline, and the computing system may map the landmarkto the virtual camera plane to get its 2D location I_v. The deviation isthen calculated as the L2 difference between I_v and H(T), i.e.∥I_v−H(T)∥{circumflex over ( )}2. The E_center term can ensure that theprojected center of the selected face on the stabilized frame followsthe estimated 2D head center H(T).

At box 374, the optimization process determines a difference between aproposed pose of the virtual camera viewpoint in the virtual space and aprevious pose of the virtual camera viewpoint in the virtual space(e.g., a difference in locations and/or orientations). With reference toFIG. 2, the optimization process may account for the distance 216between the pose 212 of the optimized virtual camera viewpoint and theprevious pose 210 of the virtual camera viewpoint, which was used togenerate the stabilized view of the previous frame 130. (This discussionsometimes refers to proposed poses of the virtual camera for simplicityof explanation, but it should be understood that such discussion isintended to cover optimization processes that may not actually testmultiple different proposed poses, and instead perform an optimizationprocess, for example, as described throughout this disclosure.)

Referring back to the equation for E_V_0(T), the operations of box 374can represent E_rotation_smoothness, the rotation smoothness term. Thisterm measures the difference between the virtual camera pose for thecurrent frame and the virtual camera pose for the previous frame.Rotation metrics such as l2 difference between the Quaternionrepresentations (a 4D vector) can be used. This term can help ensurethat the change in virtual camera pose occurs smoothly.

At box 376, the optimization process determines a difference between theproposed pose of the virtual camera viewpoint in the virtual space andthe pose of the physical camera in the virtual space. With reference toFIG. 2, the optimization process may account for the distance 218between the pose 212 of the optimized virtual camera viewpoint and thepose 214 of the physical camera.

Referring back to the equation for E_V_0(T), the operations of box 376can represent E_rotaton_following, the rotation following term. Asmentioned above, this term can measure the difference between thevirtual camera rotation (another way to reference virtual camera“orientation”) for the current frame and the real camera rotation forthe current frame, which can ensure the virtual camera rotation followsthe real camera rotation.

At box 378, the optimization process determines whether the virtualcamera has rotated too far away from the real camera. Referring back tothe equation for E_V_0(T), the operations of box 378 representE_distortion, the distortion term. This term can measure the weightedspherical angle between the virtual camera rotation and the real camerarotation: E=L(angle)*angle, where the weightL(angle)=1/(1+exp(\beta_1*(angle−\beta_0))) is a Logistic regressionthat is close to 0 if angle is smaller than the threshold \beta_0, andclose to 1 otherwise. The parameter \beta_1 controls how fast thetransition from 0 to 1 goes. This term may only become effective (i.e.,output a large value) when the virtual camera rotates too far away fromthe real camera. This term can help ensure that the virtual camera onlyrotates within a certain range from the real camera, and does not rotatefar enough to cause visually observable perspective distortion.

At box 380, the optimization process measures the change in offset tothe virtual principal point between the current frame and a previousframe (e.g., the immediately-preceding frame). Referring back to theequation for E_V_0(T), the operations of box 380 representE_offset_smoothness, the offset smoothness term. This term measures thechange of offsets to the virtual principal point between the current andprevious frames, and helps ensure that the offset changes smoothlyacross frames.

Referring back to the equation for E_V_0(T), E_undefined_pixel, theundefined pixel term (not illustrated in FIGS. 3A-E) computes atransformation to map the real image projected by P_(i,j) to P_v(T) foreach scanline, warps the video frame to the virtual camera plane, andmeasures the amount of undefined pixels in the warped frame. This termpenalizes virtual pose solutions that would cause undefined pixels. Areference amount r may be used to control the sensitivity of thepenalty, for example, so that E=amount of undefined pixel/r. Byadjusting r to a large value, this term may output small values todisable the penalty. When r is small, this term can output large valuesto dominate the entire optimization and avoid the occurrence ofundefined pixels.

The weights applied to the various terms in the equation for E_V_0(T)may change for different frames. For example, when there is no validface or there is no face detected in a frame, the weight w_1 may be setto 0, so that the optimization process does not take face stabilizationinto account when determining the pose of the virtual camera.Furthermore, the weights w_1-w_6 may be changed based on landmarkdetection confidence. For example, when the landmark detectionconfidence is low, w_1 can be decreased to avoid fitting to theunreliable landmarks, and when landmarks across frames are unstable, thesmoothness-related weights can be increased to avoid virtual cameramovement that may result from unstable landmarks. Furthermore, when thepose is large, the smoothness related weights can be increased to avoidvirtual camera moves that may result from variations to the pose.

At box 382, the computing system tests the stabilization. It may performthis test by generating a test stabilized view of the frame of the videostream using the optimized pose of the virtual camera viewpoint (box384). For instance, the computing system 220 may generate a stabilizedview of frame 230 from the location 212 of the virtual camera. Forexample, from V_0(T), the computing system may compute thetransformation to map the real image projected by P_(i,j) to P_v(T) foreach scanline.

At box 386, the computing system determines whether the test stabilizedview of the frame includes undefined pixels. If so, the computing systemmay select a different virtual camera viewpoint (box 387). In greaterdetail, the computing system may determine whether P_v(T) for eachscanline would leave any pixels undefined in the output frame. If so,the reference amount r in the undefined pixel term may be too large. Abinary search on this reference amount between a preset minimal valueand its current value may be performed, and a maximum reference amountthat does not cause undefined pixels may be selected, and theoptimization result may be used as the final virtual camera pose V(T).

If the virtual camera viewpoint would not leave any undefined pixels,for example, because the computing system 220 determines that thestabilized view of frame 230 taken from the location 212 of the virtualcamera does not include any undefined pixels, the computing system mayuse the determined virtual camera viewpoint as a final virtual cameraviewpoint (box 388).

At box 390, the computing system generates the stabilized view of theframe using the optimized pose of the virtual camera viewpoint. Forinstance, an image warping engine can load the mapping output from amotion filtering engine, and can use the output to map each pixel in theinput frame to an output frame. For instance, the computing system mayuse the final V(T) to generate the final virtual projection matrixP′_v(T), and can compute the final mapping for each scanline. This taskis common in image and graphics processing systems, and differentsolutions can be implemented based on whether the process is to beoptimized for performance or quality, or some blend of the two.

At box 392, the computing system generates the stabilized view of theframe by mapping a subset of scanlines of the frame to perspectivesviewed from the optimized pose of the virtual camera viewpoint, andinterpolates other of the scanlines from the frame. For instance,Instead of calculating P_(i,j) and mappings for every single scanline,the computing system may compute only a subset of the scanlines, and canuse interpolation in between to generate the dense mapping.

At box 394, the computing system presents the stabilized view of theframe of the video stream on a display and stores the stabilized view ofthe frame in memory. In some examples, the presentation of thestabilized view of the frame is performed in real-time while the videois being recorded, for example, before a next frame is captured orshortly after the frame is captured (e.g., before another 2, 5, 10, or15 frames is captured during an ongoing recording). In some examples,storing the stabilized view of the frame in memory can include deletingor otherwise not persistently storing the original, un-stabilized frame.As such, upon the computing system finishing a video recording, thecomputing system may store a stabilized version of the video and may notstore an unstabilized version of the video.

At box 396, the computing system repeats this process for the next frameof the video, for example, by starting the process back at box 312 withthe next frame, unless the video includes no more frames.

The techniques described herein can be used to stabilize video to reducemovement of a camera and movement of a non-face object. For example, thecomputing system may track the center of another type of moving object,such as a football being thrown or a vehicle moving across the frame,and stabilize the video to reduce not only movement of the camera butalso movement of the non-face object.

Further to the descriptions above, a user may be provided with controlsallowing the user to make an election as to both if and when systems,programs or features described herein may enable collection of userinformation (e.g., information about a location of a device). Anylocation information may be generalized as to where location informationis obtained (such as to a city, ZIP code, or state level), so that aparticular location of a user cannot be determined. Thus, the user mayhave control over what information is collected about the user, how thatinformation is used, and what information is provided to the user.Moreover, any location-determinations performed with respect to thetechnologies described herein may identify only a pose of a devicerelative to an initial pose when video recording began, not absolutegeographical location of the device.

Referring now to FIG. 4, a conceptual diagram of a system that may beused to implement the systems and methods described in this document isillustrated. In the system, mobile computing device 410 can wirelesslycommunicate with base station 440, which can provide the mobilecomputing device wireless access to numerous hosted services 460 througha network 450.

In this illustration, the mobile computing device 410 is depicted as ahandheld mobile telephone (e.g., a smartphone, or an applicationtelephone) that includes a touchscreen display device 412 for presentingcontent to a user of the mobile computing device 410 and receivingtouch-based user inputs. Other visual, tactile, and auditory outputcomponents may also be provided (e.g., LED lights, a vibrating mechanismfor tactile output, or a speaker for providing tonal, voice-generated,or recorded output), as may various different input components (e.g.,keyboard 414, physical buttons, trackballs, accelerometers, gyroscopes,and magnetometers).

Example visual output mechanism in the form of display device 412 maytake the form of a display with resistive or capacitive touchcapabilities. The display device may be for displaying video, graphics,images, and text, and for coordinating user touch input locations withthe location of displayed information so that the device 410 canassociate user contact at a location of a displayed item with the item.The mobile computing device 410 may also take alternative forms,including as a laptop computer, a tablet or slate computer, a personaldigital assistant, an embedded system (e.g., a car navigation system), adesktop personal computer, or a computerized workstation.

An example mechanism for receiving user-input includes keyboard 414,which may be a full qwerty keyboard or a traditional keypad thatincludes keys for the digits ‘0-9’, ‘*’, and ‘#.’ The keyboard 414receives input when a user physically contacts or depresses a keyboardkey. User manipulation of a trackball 416 or interaction with a trackpad enables the user to supply directional and rate of movementinformation to the mobile computing device 410 (e.g., to manipulate aposition of a cursor on the display device 412).

The mobile computing device 410 may be able to determine a position ofphysical contact with the touchscreen display device 412 (e.g., aposition of contact by a finger or a stylus). Using the touchscreen 412,various “virtual” input mechanisms may be produced, where a userinteracts with a graphical user interface element depicted on thetouchscreen 412 by contacting the graphical user interface element. Anexample of a “virtual” input mechanism is a “software keyboard,” where akeyboard is displayed on the touchscreen and a user selects keys bypressing a region of the touchscreen 412 that corresponds to each key.

The mobile computing device 410 may include mechanical or touchsensitive buttons 418 a-d. Additionally, the mobile computing device mayinclude buttons for adjusting volume output by the one or more speakers420, and a button for turning the mobile computing device on or off. Amicrophone 422 allows the mobile computing device 410 to convert audiblesounds into an electrical signal that may be digitally encoded andstored in computer-readable memory, or transmitted to another computingdevice. The mobile computing device 410 may also include a digitalcompass, an accelerometer, proximity sensors, and ambient light sensors.

An operating system may provide an interface between the mobilecomputing device's hardware (e.g., the input/output mechanisms and aprocessor executing instructions retrieved from computer-readablemedium) and software. Example operating systems include ANDROID, CHROME,10S, MAC OS X, WINDOWS 7, WINDOWS PHONE 7, SYMBIAN, BLACKBERRY, WEBOS, avariety of UNIX operating systems; or a proprietary operating system forcomputerized devices. The operating system may provide a platform forthe execution of application programs that facilitate interactionbetween the computing device and a user.

The mobile computing device 410 may present a graphical user interfacewith the touchscreen 412. A graphical user interface is a collection ofone or more graphical interface elements and may be static (e.g., thedisplay appears to remain the same over a period of time), or may bedynamic (e.g., the graphical user interface includes graphical interfaceelements that animate without user input).

A graphical interface element may be text, lines, shapes, images, orcombinations thereof. For example, a graphical interface element may bean icon that is displayed on the desktop and the icon's associated text.In some examples, a graphical interface element is selectable withuser-input. For example, a user may select a graphical interface elementby pressing a region of the touchscreen that corresponds to a display ofthe graphical interface element. In some examples, the user maymanipulate a trackball to highlight a single graphical interface elementas having focus. User-selection of a graphical interface element mayinvoke a pre-defined action by the mobile computing device. In someexamples, selectable graphical interface elements further oralternatively correspond to a button on the keyboard 404. User-selectionof the button may invoke the pre-defined action.

In some examples, the operating system provides a “desktop” graphicaluser interface that is displayed after turning on the mobile computingdevice 410, after activating the mobile computing device 410 from asleep state, after “unlocking” the mobile computing device 410, or afterreceiving user-selection of the “home” button 418 c. The desktopgraphical user interface may display several graphical interfaceelements that, when selected, invoke corresponding application programs.An invoked application program may present a graphical interface thatreplaces the desktop graphical user interface until the applicationprogram terminates or is hidden from view.

User-input may influence an executing sequence of mobile computingdevice 410 operations. For example, a single-action user input (e.g., asingle tap of the touchscreen, swipe across the touchscreen, contactwith a button, or combination of these occurring at a same time) mayinvoke an operation that changes a display of the user interface.Without the user-input, the user interface may not have changed at aparticular time. For example, a multi-touch user input with thetouchscreen 412 may invoke a mapping application to “zoom-in” on alocation, even though the mapping application may have by defaultzoomed-in after several seconds.

The desktop graphical interface can also display “widgets.” A widget isone or more graphical interface elements that are associated with anapplication program that is executing, and that display on the desktopcontent controlled by the executing application program. A widget'sapplication program may launch as the mobile device turns on. Further, awidget may not take focus of the full display. Instead, a widget mayonly “own” a small portion of the desktop, displaying content andreceiving touchscreen user-input within the portion of the desktop.

The mobile computing device 410 may include one or morelocation-identification mechanisms. A location-identification mechanismmay include a collection of hardware and software that provides theoperating system and application programs an estimate of the mobiledevice's geographical position. A location-identification mechanism mayemploy satellite-based positioning techniques, base station transmittingantenna identification, multiple base station triangulation, internetaccess point IP location determinations, inferential identification of auser's position based on search engine queries, and user-suppliedidentification of location (e.g., by receiving user a “check in” to alocation).

The mobile computing device 410 may include other applications,computing sub-systems, and hardware. A call handling unit may receive anindication of an incoming telephone call and provide a user thecapability to answer the incoming telephone call. A media player mayallow a user to listen to music or play movies that are stored in localmemory of the mobile computing device 410. The mobile device 410 mayinclude a digital camera sensor, and corresponding image and videocapture and editing software. An internet browser may enable the user toview content from a web page by typing in an addresses corresponding tothe web page or selecting a link to the web page.

The mobile computing device 410 may include an antenna to wirelesslycommunicate information with the base station 440. The base station 440may be one of many base stations in a collection of base stations (e.g.,a mobile telephone cellular network) that enables the mobile computingdevice 410 to maintain communication with a network 450 as the mobilecomputing device is geographically moved. The computing device 410 mayalternatively or additionally communicate with the network 450 through aWi-Fi router or a wired connection (e.g., ETHERNET, USB, or FIREWIRE).The computing device 410 may also wirelessly communicate with othercomputing devices using BLUETOOTH protocols, or may employ an ad-hocwireless network.

A service provider that operates the network of base stations mayconnect the mobile computing device 410 to the network 450 to enablecommunication between the mobile computing device 410 and othercomputing systems that provide services 460. Although the services 460may be provided over different networks (e.g., the service provider'sinternal network, the Public Switched Telephone Network, and theInternet), network 450 is illustrated as a single network. The serviceprovider may operate a server system 452 that routes information packetsand voice data between the mobile computing device 410 and computingsystems associated with the services 460.

The network 450 may connect the mobile computing device 410 to thePublic Switched Telephone Network (PSTN) 462 in order to establish voiceor fax communication between the mobile computing device 410 and anothercomputing device. For example, the service provider server system 452may receive an indication from the PSTN 462 of an incoming call for themobile computing device 410. Conversely, the mobile computing device 410may send a communication to the service provider server system 452initiating a telephone call using a telephone number that is associatedwith a device accessible through the PSTN 462.

The network 450 may connect the mobile computing device 410 with a Voiceover Internet Protocol (VoIP) service 464 that routes voicecommunications over an IP network, as opposed to the PSTN. For example,a user of the mobile computing device 410 may invoke a VoIP applicationand initiate a call using the program. The service provider serversystem 452 may forward voice data from the call to a VoIP service, whichmay route the call over the internet to a corresponding computingdevice, potentially using the PSTN for a final leg of the connection.

An application store 466 may provide a user of the mobile computingdevice 410 the ability to browse a list of remotely stored applicationprograms that the user may download over the network 450 and install onthe mobile computing device 410. The application store 466 may serve asa repository of applications developed by third-party applicationdevelopers. An application program that is installed on the mobilecomputing device 410 may be able to communicate over the network 450with server systems that are designated for the application program. Forexample, a VoIP application program may be downloaded from theApplication Store 466, enabling the user to communicate with the VoIPservice 464.

The mobile computing device 410 may access content on the internet 468through network 450. For example, a user of the mobile computing device410 may invoke a web browser application that requests data from remotecomputing devices that are accessible at designated universal resourcelocations. In various examples, some of the services 460 are accessibleover the internet.

The mobile computing device may communicate with a personal computer470. For example, the personal computer 470 may be the home computer fora user of the mobile computing device 410. Thus, the user may be able tostream media from his personal computer 470. The user may also view thefile structure of his personal computer 470, and transmit selecteddocuments between the computerized devices.

A voice recognition service 472 may receive voice communication datarecorded with the mobile computing device's microphone 422, andtranslate the voice communication into corresponding textual data. Insome examples, the translated text is provided to a search engine as aweb query, and responsive search engine search results are transmittedto the mobile computing device 410.

The mobile computing device 410 may communicate with a social network474. The social network may include numerous members, some of which haveagreed to be related as acquaintances. Application programs on themobile computing device 410 may access the social network 474 toretrieve information based on the acquaintances of the user of themobile computing device. For example, an “address book” applicationprogram may retrieve telephone numbers for the user's acquaintances. Invarious examples, content may be delivered to the mobile computingdevice 410 based on social network distances from the user to othermembers in a social network graph of members and connectingrelationships. For example, advertisement and news article content maybe selected for the user based on a level of interaction with suchcontent by members that are “close” to the user (e.g., members that are“friends” or “friends of friends”).

The mobile computing device 410 may access a personal set of contacts476 through network 450. Each contact may identify an individual andinclude information about that individual (e.g., a phone number, anemail address, and a birthday). Because the set of contacts is hostedremotely to the mobile computing device 410, the user may access andmaintain the contacts 476 across several devices as a common set ofcontacts.

The mobile computing device 410 may access cloud-based applicationprograms 478. Cloud-computing provides application programs (e.g., aword processor or an email program) that are hosted remotely from themobile computing device 410, and may be accessed by the device 410 usinga web browser or a dedicated program. Example cloud-based applicationprograms include GOOGLE DOCS word processor and spreadsheet service,GOOGLE GMAIL webmail service, and PICASA picture manager.

Mapping service 480 can provide the mobile computing device 410 withstreet maps, route planning information, and satellite images. Anexample mapping service is GOOGLE MAPS. The mapping service 480 may alsoreceive queries and return location-specific results. For example, themobile computing device 410 may send an estimated location of the mobilecomputing device and a user-entered query for “pizza places” to themapping service 480. The mapping service 480 may return a street mapwith “markers” superimposed on the map that identify geographicallocations of nearby “pizza places.”

Turn-by-turn service 482 may provide the mobile computing device 410with turn-by-turn directions to a user-supplied destination. Forexample, the turn-by-turn service 482 may stream to device 410 astreet-level view of an estimated location of the device, along withdata for providing audio commands and superimposing arrows that direct auser of the device 410 to the destination.

Various forms of streaming media 484 may be requested by the mobilecomputing device 410. For example, computing device 410 may request astream for a pre-recorded video file, a live television program, or alive radio program. Example services that provide streaming mediainclude YOUTUBE and PANDORA.

A micro-blogging service 486 may receive from the mobile computingdevice 410 a user-input post that does not identify recipients of thepost. The micro-blogging service 486 may disseminate the post to othermembers of the micro-blogging service 486 that agreed to subscribe tothe user.

A search engine 488 may receive user-entered textual or verbal queriesfrom the mobile computing device 410, determine a set ofinternet-accessible documents that are responsive to the query, andprovide to the device 410 information to display a list of searchresults for the responsive documents. In examples where a verbal queryis received, the voice recognition service 472 may translate thereceived audio into a textual query that is sent to the search engine.

These and other services may be implemented in a server system 490. Aserver system may be a combination of hardware and software thatprovides a service or a set of services. For example, a set ofphysically separate and networked computerized devices may operatetogether as a logical server system unit to handle the operationsnecessary to offer a service to hundreds of computing devices. A serversystem is also referred to herein as a computing system.

In various implementations, operations that are performed “in responseto” or “as a consequence of” another operation (e.g., a determination oran identification) are not performed if the prior operation isunsuccessful (e.g., if the determination was not performed). Operationsthat are performed “automatically” are operations that are performedwithout user intervention (e.g., intervening user input). Features inthis document that are described with conditional language may describeimplementations that are optional. In some examples, “transmitting” froma first device to a second device includes the first device placing datainto a network for receipt by the second device, but may not include thesecond device receiving the data. Conversely, “receiving” from a firstdevice may include receiving the data from a network, but may notinclude the first device transmitting the data.

“Determining” by a computing system can include the computing systemrequesting that another device perform the determination and supply theresults to the computing system. Moreover, “displaying” or “presenting”by a computing system can include the computing system sending data forcausing another device to display or present the referenced information.

FIG. 5 is a block diagram of computing devices 500, 550 that may be usedto implement the systems and methods described in this document, aseither a client or as a server or plurality of servers. Computing device500 is intended to represent various forms of digital computers, such aslaptops, desktops, workstations, personal digital assistants, servers,blade servers, mainframes, and other appropriate computers. Computingdevice 550 is intended to represent various forms of mobile devices,such as personal digital assistants, cellular telephones, smartphones,and other similar computing devices. The components shown here, theirconnections and relationships, and their functions, are meant to beexamples only, and are not meant to limit implementations describedand/or claimed in this document.

Computing device 500 includes a processor 502, memory 504, a storagedevice 506, a high-speed interface 508 connecting to memory 504 andhigh-speed expansion ports 510, and a low speed interface 512 connectingto low speed bus 514 and storage device 506. Each of the components 502,504, 506, 508, 510, and 512, are interconnected using various busses,and may be mounted on a common motherboard or in other manners asappropriate. The processor 502 can process instructions for executionwithin the computing device 500, including instructions stored in thememory 504 or on the storage device 506 to display graphical informationfor a GUI on an external input/output device, such as display 516coupled to high-speed interface 508. In other implementations, multipleprocessors and/or multiple buses may be used, as appropriate, along withmultiple memories and types of memory. Also, multiple computing devices500 may be connected, with each device providing portions of thenecessary operations (e.g., as a server bank, a group of blade servers,or a multi-processor system).

The memory 504 stores information within the computing device 500. Inone implementation, the memory 504 is a volatile memory unit or units.In another implementation, the memory 504 is a non-volatile memory unitor units. The memory 504 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 506 is capable of providing mass storage for thecomputing device 500. In one implementation, the storage device 506 maybe or contain a computer-readable medium, such as a floppy disk device,a hard disk device, an optical disk device, or a tape device, a flashmemory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product may also containinstructions that, when executed, perform one or more methods, such asthose described above. The information carrier is a computer- ormachine-readable medium, such as the memory 504, the storage device 506,or memory on processor 502.

The high-speed controller 508 manages bandwidth-intensive operations forthe computing device 500, while the low speed controller 512 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In one implementation, the high-speed controller 508 iscoupled to memory 504, display 516 (e.g., through a graphics processoror accelerator), and to high-speed expansion ports 510, which may acceptvarious expansion cards (not shown). In the implementation, low-speedcontroller 512 is coupled to storage device 506 and low-speed expansionport 514. The low-speed expansion port, which may include variouscommunication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet)may be coupled to one or more input/output devices, such as a keyboard,a pointing device, a scanner, or a networking device such as a switch orrouter, e.g., through a network adapter.

The computing device 500 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 520, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 524. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 522. Alternatively, components from computing device 500 may becombined with other components in a mobile device (not shown), such asdevice 550. Each of such devices may contain one or more of computingdevice 500, 550, and an entire system may be made up of multiplecomputing devices 500, 550 communicating with each other.

Computing device 550 includes a processor 552, memory 564, aninput/output device such as a display 554, a communication interface566, and a transceiver 568, among other components. The device 550 mayalso be provided with a storage device, such as a microdrive or otherdevice, to provide additional storage. Each of the components 550, 552,564, 554, 566, and 568, are interconnected using various buses, andseveral of the components may be mounted on a common motherboard or inother manners as appropriate.

The processor 552 can execute instructions within the computing device550, including instructions stored in the memory 564. The processor maybe implemented as a chipset of chips that include separate and multipleanalog and digital processors. Additionally, the processor may beimplemented using any of a number of architectures. For example, theprocessor may be a CISC (Complex Instruction Set Computers) processor, aRISC (Reduced Instruction Set Computer) processor, or a MISC (MinimalInstruction Set Computer) processor. The processor may provide, forexample, for coordination of the other components of the device 550,such as control of user interfaces, applications run by device 550, andwireless communication by device 550.

Processor 552 may communicate with a user through control interface 558and display interface 556 coupled to a display 554. The display 554 maybe, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display)display or an OLED (Organic Light Emitting Diode) display, or otherappropriate display technology. The display interface 556 may compriseappropriate circuitry for driving the display 554 to present graphicaland other information to a user. The control interface 558 may receivecommands from a user and convert them for submission to the processor552. In addition, an external interface 562 may be provide incommunication with processor 552, so as to enable near areacommunication of device 550 with other devices. External interface 562may provided, for example, for wired communication in someimplementations, or for wireless communication in other implementations,and multiple interfaces may also be used.

The memory 564 stores information within the computing device 550. Thememory 564 can be implemented as one or more of a computer-readablemedium or media, a volatile memory unit or units, or a non-volatilememory unit or units. Expansion memory 574 may also be provided andconnected to device 550 through expansion interface 572, which mayinclude, for example, a SIMM (Single In Line Memory Module) cardinterface. Such expansion memory 574 may provide extra storage space fordevice 550, or may also store applications or other information fordevice 550. Specifically, expansion memory 574 may include instructionsto carry out or supplement the processes described above, and mayinclude secure information also. Thus, for example, expansion memory 574may be provide as a security module for device 550, and may beprogrammed with instructions that permit secure use of device 550. Inaddition, secure applications may be provided via the SIMM cards, alongwith additional information, such as placing identifying information onthe SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory,as discussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described above. The information carrier is acomputer- or machine-readable medium, such as the memory 564, expansionmemory 574, or memory on processor 552 that may be received, forexample, over transceiver 568 or external interface 562.

Device 550 may communicate wirelessly through communication interface566, which may include digital signal processing circuitry wherenecessary. Communication interface 566 may provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication may occur, for example, through radio-frequencytransceiver 568. In addition, short-range communication may occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, GPS (Global Positioning System) receiver module 570 mayprovide additional navigation- and location-related wireless data todevice 550, which may be used as appropriate by applications running ondevice 550.

Device 550 may also communicate audibly using audio codec 560, which mayreceive spoken information from a user and convert it to usable digitalinformation. Audio codec 560 may likewise generate audible sound for auser, such as through a speaker, e.g., in a handset of device 550. Suchsound may include sound from voice telephone calls, may include recordedsound (e.g., voice messages, music files, etc.) and may also includesound generated by applications operating on device 550.

The computing device 550 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 580. It may also be implemented as part of asmartphone 582, personal digital assistant, or other similar mobiledevice.

Additionally computing device 500 or 550 can include Universal SerialBus (USB) flash drives. The USB flash drives may store operating systemsand other applications. The USB flash drives can include input/outputcomponents, such as a wireless transmitter or USB connector that may beinserted into a USB port of another computing device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), peer-to-peernetworks (having ad-hoc or static members), grid computinginfrastructures, and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Although a few implementations have been described in detail above,other modifications are possible. Moreover, other mechanisms forperforming the systems and methods described in this document may beused. In addition, the logic flows depicted in the figures do notrequire the particular order shown, or sequential order, to achievedesirable results. Other steps may be provided, or steps may beeliminated, from the described flows, and other components may be addedto, or removed from, the described systems. Accordingly, otherimplementations are within the scope of the following claims.

1. A computer-implemented video stabilization method, comprising:receiving, by a computing system, a video stream that includes multipleframes and that was captured by a physical camera; determining, by thecomputing system and in a frame of the video stream that was captured bythe physical camera, a location of a feature of an object that isdepicted in the frame; determining, by the computing system and in aprevious frame of the video stream that was captured by the physicalcamera, a previous location of the feature of the object; determining,by the computing system, a stabilized location of the feature, takinginto account the previous location of the feature in the previous frameand the location of the feature in the frame; determining, by thecomputing system and using information received from a movement ororientation sensor coupled to the physical camera, a pose of thephysical camera in a virtual space; mapping, by the computing system,the frame of the video stream that was captured by the physical camerainto the virtual space; determining, by the computing system, anoptimized pose of a virtual camera viewpoint in the virtual space fromwhich to generate a stabilized view of the frame, using an optimizationprocess that accounts for a difference between the stabilized locationof the feature and a location of the feature in a stabilized view of theframe viewed from a potential pose of the virtual camera viewpoint; andgenerating, by the computing system, the stabilized view of the frameusing the optimized pose of the virtual camera viewpoint in the virtualcamera space, wherein the optimized pose of the virtual camera viewpointhas a different location and rotation in the virtual space than the poseof the physical camera.
 2. The computer-implemented video stabilizationmethod of claim 1, further comprising: presenting, by the computingsystem, the stabilized view of the frame on a display of the computingsystem.
 3. The computer-implemented video stabilization method of claim1, wherein the movement or orientation sensor comprises a gyroscope. 4.The computer-implemented video stabilization method of claim 1, wherein:the computing system determines the location of the feature of theobject that is depicted in the frame based on locations of multiplerespective object landmarks that are depicted in the frame; and thecomputing system determines the difference between the stabilizedlocation of the feature and the location of the feature in thestabilized view of the frame by measuring deviations between locationsof the multiple object landmarks in the stabilized view of the frame andthe stabilized location of the feature.
 5. The computer-implementedvideo stabilization method of claim 1, wherein the optimization processcomprises a non-linear computational solver that optimizes values formultiple respective variables.
 6. The computer-implemented videostabilization method of claim 1, wherein the optimization process:determines an amount of undefined pixels in the stabilized view of theframe that is generated using the potential pose of the virtual cameraview point in the virtual space.
 7. The computer-implemented videostabilization method of claim 1, wherein the optimization process:determines a difference between: (a) an offset of a principal point ofthe stabilized view of the frame that is generated using the potentialpose of the virtual camera view point in the virtual space, and (b) anoffset of a previous principal point of a previous stabilized view ofthe frame that was generated using the previous pose of the virtualcamera viewpoint in the virtual space.
 8. The computer-implemented videostabilization method of claim 1, wherein generating the stabilized viewof the frame includes mapping a subset of scanlines of the frame toperspectives viewed from the optimized pose of the virtual cameraviewpoint, and interpolating other of the scanlines of the frame.
 9. Thecomputer-implemented video stabilization method of claim 1, furthercomprising: selecting the object that is depicted in the frame of thevideo stream that was captured by the physical camera as an object totrack from among multiple objects depicted in the frame of the videostream that was captured by the physical camera by: selecting the objectbased on sizes of each of the multiple objects, selecting the objectbased on distances of each of the multiple objects to a center of theframe, or selecting the object based on distances between an objectselected for tracking in a previous frame and each of the multipleobjects.
 10. (canceled)
 11. A computerized system, comprising: a camera;a motion or orientation sensor physically coupled to the camera; one ormore processors; one or more non-transitory computer-readable devicesincluding instructions that, when executed by the one or moreprocessors, cause performance of operations that include: receiving, bya computing system, a video stream that includes multiple frames andthat was captured by a physical camera; determining, by the computingsystem and in a frame of the video stream that was captured by thephysical camera, a location of a feature of an object that is depictedin the frame; determining, by the computing system and in a previousframe of the video stream that was captured by the physical camera, aprevious location of the feature of the object; determining, by thecomputing system, a stabilized location of the feature, taking intoaccount the previous location of the feature in the previous frame andthe location of the feature in the frame; determining, by the computingsystem and using information received from a movement or orientationsensor coupled to the physical camera, a pose of the physical camera ina virtual space; mapping, by the computing system, the frame of thevideo stream that was captured by the physical camera into the virtualspace; determining, by the computing system, an optimized pose of avirtual camera viewpoint in the virtual space from which to generate astabilized view of the frame, using an optimization process thataccounts for a difference between the stabilized location of the featureand a location of the feature in a stabilized view of the frame viewedfrom a potential pose of the virtual camera viewpoint; and generating,by the computing system, the stabilized view of the frame using theoptimized pose of the virtual camera viewpoint in the virtual cameraspace, wherein the optimized pose of the virtual camera viewpoint has adifferent location and rotation in the virtual space than the pose ofthe physical camera.
 12. The computerized system of claim 11, whereinthe operations further comprise: presenting, by the computing system,the stabilized view of the frame on a display of the computing system.13. The computerized system of claim 11, wherein the movement ororientation sensor comprises a gyroscope.
 14. The computerized system ofclaim 11, wherein: the computing system determines the location of thefeature of the object that is depicted in the frame based on locationsof multiple respective object landmarks that are depicted in the frame;and the computing system determines the difference between thestabilized location of the feature and the location of the feature inthe stabilized view of the frame by measuring deviations betweenlocations of the multiple object landmarks in the stabilized view of theframe and the stabilized location of the feature.
 15. The computerizedsystem of claim 11, wherein the optimization process comprises anon-linear computational solver that optimizes values for multiplerespective variables.
 16. The computerized system of claim 11, whereinthe optimization process: determines an amount of undefined pixels inthe stabilized view of the frame that is generated using the potentialpose of the virtual camera view point in the virtual space.
 17. Thecomputerized system of claim 11, wherein the optimization process:determines a difference between: (a) an offset of a principal point ofthe stabilized view of the frame that is generated using the potentialpose of the virtual camera view point in the virtual space, and (b) anoffset of a previous principal point of a previous stabilized view ofthe frame that was generated using the previous pose of the virtualcamera viewpoint in the virtual space.
 18. The computerized system ofclaim 11, wherein generating the stabilized view of the frame includesmapping a subset of scanlines of the frame to perspectives viewed fromthe optimized pose of the virtual camera viewpoint, and interpolatingother of the scanlines of the frame.
 19. The computerized system ofclaim 11, wherein the operations further comprise: selecting the objectthat is depicted in the frame of the video stream that was captured bythe physical camera as an object to track from among multiple objectsdepicted in the frame of the video stream that was captured by thephysical camera by: selecting the object based on sizes of each of themultiple objects, selecting the object based on distances of each of themultiple objects to a center of the frame, or selecting the object basedon distances between an object selected for tracking in a previous frameand each of the multiple objects.
 20. (canceled)
 21. Thecomputer-implemented method of claim 1, wherein the optimization processaccounts for a difference between the potential pose of the virtualcamera viewpoint in the virtual space and a previous pose of the virtualcamera viewpoint in the virtual space.
 22. The computer-implementedmethod of claim 1, wherein the optimization process accounts for adifference between the potential pose of the virtual camera viewpoint inthe virtual space and the pose of the physical camera in the virtualspace